@InProceedings{8081254,
  author = {Y. K. Benkouider and M. S. Karoui and Y. {De ville} and S. Hosseini},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data},
  year = {2017},
  pages = {483-487},
  abstract = {In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between these data and hyperspectral ones. The considered multispectral variables are also used to initialize the proposed algorithm. Tests, using synthetic data, are carried out to assess the performance of our algorithm and its robustness to spectral variability between the processed data. The obtained results are compared to those of state of the art methods. These tests prove that the proposed algorithm outperforms all other used approaches.},
  keywords = {geophysical image processing;hyperspectral imaging;matrix decomposition;hyperspectral ones;considered multispectral variables;synthetic data;processed data;multiplicative nonnegative matrix factorization method;multispectral data;linearly unmixing hyperspectral images;considered hyperspectral image;multiplicative update rules;Hyperspectral imaging;Signal processing algorithms;Algorithm design and analysis;Cost function;Spatial resolution;Data models;Hyper/multispectral imaging;linear unmixing;multiplicative update rule;nonnegative matrix factorization;spectral degradation model},
  doi = {10.23919/EUSIPCO.2017.8081254},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570346773.pdf},
}

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S.","De ville, Y.","Hosseini, S."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Y.","K."],"propositions":[],"lastnames":["Benkouider"],"suffixes":[]},{"firstnames":["M.","S."],"propositions":[],"lastnames":["Karoui"],"suffixes":[]},{"firstnames":["Y."],"propositions":[],"lastnames":["De ville"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Hosseini"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data","year":"2017","pages":"483-487","abstract":"In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between these data and hyperspectral ones. The considered multispectral variables are also used to initialize the proposed algorithm. Tests, using synthetic data, are carried out to assess the performance of our algorithm and its robustness to spectral variability between the processed data. The obtained results are compared to those of state of the art methods. 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Hosseini},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data},\n year = {2017},\n pages = {483-487},\n abstract = {In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between these data and hyperspectral ones. The considered multispectral variables are also used to initialize the proposed algorithm. Tests, using synthetic data, are carried out to assess the performance of our algorithm and its robustness to spectral variability between the processed data. 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